The goal of this paper is to present a theoretical and practical introduction to generalized
eigendecomposition (GED), which is a robust and flexible framework used for dimension …

Objective. Supervised learning paradigms are often limited by the amount of labeled data
that is available. This phenomenon is particularly problematic in clinically-relevant data …

Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-
computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG …

Electroencephalography (EEG) is a diagnostic test that records and measures the electrical
activity of the human brain. Research investigating human behaviors and conditions using …

Population-level modeling can define quantitative measures of individual aging by applying
machine learning to large volumes of brain images. These measures of brain age, obtained …

When dealing with electro or magnetoencephalography records, many supervised
prediction tasks are solved by working with covariance matrices to summarize the signals …

Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining
experimental control, but dynamic and interactive stimuli pose methodological challenges …

Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet,
when building predictive models from brain data, it is often unclear how electrophysiology …

Diagnostic heterogeneity within and across psychotic and affective disorders challenges
accurate treatment selection, particularly in the early stages. Delineation of shared and …

Building machine learning models using EEG recorded outside of the laboratory setting
requires methods robust to noisy data and randomly missing channels. This need is …